(583, 11)
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
<class 'pandas.core.frame.DataFrame'> RangeIndex: 583 entries, 0 to 582 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 583 non-null int64 1 Gender 583 non-null object 2 Total_Bilirubin 583 non-null float64 3 Direct_Bilirubin 583 non-null float64 4 Alkaline_Phosphotase 583 non-null int64 5 Alamine_Aminotransferase 583 non-null int64 6 Aspartate_Aminotransferase 583 non-null int64 7 Total_Protiens 583 non-null float64 8 Albumin 583 non-null float64 9 Albumin_and_Globulin_Ratio 579 non-null float64 10 Dataset 583 non-null int64 dtypes: float64(5), int64(5), object(1) memory usage: 50.2+ KB
Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',
'Alkaline_Phosphotase', 'Alamine_Aminotransferase',
'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',
'Albumin_and_Globulin_Ratio', 'Dataset'],
dtype='object')
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 |
| 579 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 580 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 581 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 582 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 |
583 rows × 11 columns
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | False | False | False | False | False | False | False | False | False | False | False |
| 1 | False | False | False | False | False | False | False | False | False | False | False |
| 2 | False | False | False | False | False | False | False | False | False | False | False |
| 3 | False | False | False | False | False | False | False | False | False | False | False |
| 4 | False | False | False | False | False | False | False | False | False | False | False |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | False | False | False | False | False | False | False | False | False | False | False |
| 579 | False | False | False | False | False | False | False | False | False | False | False |
| 580 | False | False | False | False | False | False | False | False | False | False | False |
| 581 | False | False | False | False | False | False | False | False | False | False | False |
| 582 | False | False | False | False | False | False | False | False | False | False | False |
583 rows × 11 columns
Age 0 Gender 0 Total_Bilirubin 0 Direct_Bilirubin 0 Alkaline_Phosphotase 0 Alamine_Aminotransferase 0 Aspartate_Aminotransferase 0 Total_Protiens 0 Albumin 0 Albumin_and_Globulin_Ratio 4 Dataset 0 dtype: int64
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 209 | 45 | Female | 0.9 | 0.3 | 189 | 23 | 33 | 6.6 | 3.9 | NaN | 1 |
| 241 | 51 | Male | 0.8 | 0.2 | 230 | 24 | 46 | 6.5 | 3.1 | NaN | 1 |
| 253 | 35 | Female | 0.6 | 0.2 | 180 | 12 | 15 | 5.2 | 2.7 | NaN | 2 |
| 312 | 27 | Male | 1.3 | 0.6 | 106 | 25 | 54 | 8.5 | 4.8 | NaN | 2 |
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 |
| 579 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 580 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 581 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 582 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 |
579 rows × 11 columns
Age 0 Gender 0 Total_Bilirubin 0 Direct_Bilirubin 0 Alkaline_Phosphotase 0 Alamine_Aminotransferase 0 Aspartate_Aminotransferase 0 Total_Protiens 0 Albumin 0 Albumin_and_Globulin_Ratio 0 Dataset 0 dtype: int64
Age 0 Total_Bilirubin 0 Direct_Bilirubin 0 Alkaline_Phosphotase 0 Alamine_Aminotransferase 0 Aspartate_Aminotransferase 0 Total_Protiens 0 Albumin 0 Albumin_and_Globulin_Ratio 0 Dataset 0 dtype: int64
13
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 19 | 40 | Female | 0.9 | 0.3 | 293 | 232 | 245 | 6.8 | 3.1 | 0.80 | 1 |
| 26 | 34 | Male | 4.1 | 2.0 | 289 | 875 | 731 | 5.0 | 2.7 | 1.10 | 1 |
| 34 | 38 | Female | 2.6 | 1.2 | 410 | 59 | 57 | 5.6 | 3.0 | 0.80 | 2 |
| 55 | 42 | Male | 8.9 | 4.5 | 272 | 31 | 61 | 5.8 | 2.0 | 0.50 | 1 |
| 62 | 58 | Male | 1.0 | 0.5 | 158 | 37 | 43 | 7.2 | 3.6 | 1.00 | 1 |
| 106 | 36 | Male | 5.3 | 2.3 | 145 | 32 | 92 | 5.1 | 2.6 | 1.00 | 2 |
| 108 | 36 | Male | 0.8 | 0.2 | 158 | 29 | 39 | 6.0 | 2.2 | 0.50 | 2 |
| 138 | 18 | Male | 0.8 | 0.2 | 282 | 72 | 140 | 5.5 | 2.5 | 0.80 | 1 |
| 143 | 30 | Male | 1.6 | 0.4 | 332 | 84 | 139 | 5.6 | 2.7 | 0.90 | 1 |
| 158 | 72 | Male | 0.7 | 0.1 | 196 | 20 | 35 | 5.8 | 2.0 | 0.50 | 1 |
| 164 | 39 | Male | 1.9 | 0.9 | 180 | 42 | 62 | 7.4 | 4.3 | 1.38 | 1 |
| 174 | 31 | Male | 0.6 | 0.1 | 175 | 48 | 34 | 6.0 | 3.7 | 1.60 | 1 |
| 201 | 49 | Male | 0.6 | 0.1 | 218 | 50 | 53 | 5.0 | 2.4 | 0.90 | 1 |
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 |
| 562 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 563 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 564 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 565 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 |
566 rows × 11 columns
(566, 11)
Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',
'Alkaline_Phosphotase', 'Alamine_Aminotransferase',
'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',
'Albumin_and_Globulin_Ratio', 'Dataset'],
dtype='object')
Text(0.5, 0.98, 'Boxplot of Total Bilirubin | Direct Bilirubin')
Text(0.5, 0.98, 'Boxplot of Total Protiens | Albumin | Albumin and Globulin Ratio')
Text(0.5, 0.98, 'Boxplot of Aspartate Aminotransferase | Alamine Aminotransferase | Alkaline Phosphotase')
Dataset Column
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Details | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | Patient with liver disease |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | Patient with liver disease |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | Patient with liver disease |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | Patient with liver disease |
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Details | Gender_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | Patient with liver disease | 0 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | Patient with liver disease | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | Patient with liver disease | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | Patient with liver disease | 1 |
['Age', 'Total_Bilirubin', 'Direct_Bilirubin', 'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin', 'Albumin_and_Globulin_Ratio']
| Age | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 |
| 1 | 62 | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 |
| 2 | 62 | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 |
| 3 | 58 | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 |
| 4 | 72 | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 |
| 562 | 40 | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 |
| 563 | 52 | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 |
| 564 | 31 | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 |
| 565 | 38 | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 |
566 rows × 9 columns
['Gender', 'Dataset']
| Gender | Dataset | |
|---|---|---|
| 0 | Female | 1 |
| 1 | Male | 1 |
| 2 | Male | 1 |
| 3 | Male | 1 |
| 4 | Male | 1 |
| ... | ... | ... |
| 561 | Male | 2 |
| 562 | Male | 1 |
| 563 | Male | 1 |
| 564 | Male | 1 |
| 565 | Male | 2 |
566 rows × 2 columns
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Age | 566.0 | 44.886926 | 16.274893 | 4.0 | 33.0 | 45.00 | 58.00 | 90.0 |
| Total_Bilirubin | 566.0 | 3.338869 | 6.286728 | 0.4 | 0.8 | 1.00 | 2.60 | 75.0 |
| Direct_Bilirubin | 566.0 | 1.505830 | 2.841485 | 0.1 | 0.2 | 0.30 | 1.30 | 19.7 |
| Alkaline_Phosphotase | 566.0 | 292.567138 | 245.936559 | 63.0 | 176.0 | 208.00 | 298.00 | 2110.0 |
| Alamine_Aminotransferase | 566.0 | 80.143110 | 182.044881 | 10.0 | 23.0 | 35.00 | 60.75 | 2000.0 |
| Aspartate_Aminotransferase | 566.0 | 109.892226 | 291.841897 | 10.0 | 25.0 | 41.00 | 87.00 | 4929.0 |
| Total_Protiens | 566.0 | 6.494876 | 1.087512 | 2.7 | 5.8 | 6.60 | 7.20 | 9.6 |
| Albumin | 566.0 | 3.145583 | 0.795745 | 0.9 | 2.6 | 3.10 | 3.80 | 5.5 |
| Albumin_and_Globulin_Ratio | 566.0 | 0.948004 | 0.319635 | 0.3 | 0.7 | 0.95 | 1.10 | 2.8 |
| Dataset | 566.0 | 1.286219 | 0.452393 | 1.0 | 1.0 | 1.00 | 2.00 | 2.0 |
| Gender_Binary | 566.0 | 0.756184 | 0.429763 | 0.0 | 1.0 | 1.00 | 1.00 | 1.0 |
| Age | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|
| Age | 1.000000 | 0.011763 | 0.007529 | 0.080425 | -0.086883 | -0.019910 | -0.187461 | -0.265924 | -0.216408 | -0.137351 |
| Total_Bilirubin | 0.011763 | 1.000000 | 0.874618 | 0.206669 | 0.214065 | 0.237831 | -0.008099 | -0.222250 | -0.206267 | -0.220208 |
| Direct_Bilirubin | 0.007529 | 0.874618 | 1.000000 | 0.234939 | 0.233894 | 0.257544 | -0.000139 | -0.228531 | -0.200125 | -0.246046 |
| Alkaline_Phosphotase | 0.080425 | 0.206669 | 0.234939 | 1.000000 | 0.125680 | 0.167196 | -0.028514 | -0.165453 | -0.234166 | -0.184866 |
| Alamine_Aminotransferase | -0.086883 | 0.214065 | 0.233894 | 0.125680 | 1.000000 | 0.791966 | -0.042518 | -0.029742 | -0.002375 | -0.163416 |
| Aspartate_Aminotransferase | -0.019910 | 0.237831 | 0.257544 | 0.167196 | 0.791966 | 1.000000 | -0.025645 | -0.085290 | -0.070040 | -0.151934 |
| Total_Protiens | -0.187461 | -0.008099 | -0.000139 | -0.028514 | -0.042518 | -0.025645 | 1.000000 | 0.784053 | 0.234887 | 0.035008 |
| Albumin | -0.265924 | -0.222250 | -0.228531 | -0.165453 | -0.029742 | -0.085290 | 0.784053 | 1.000000 | 0.689632 | 0.161388 |
| Albumin_and_Globulin_Ratio | -0.216408 | -0.206267 | -0.200125 | -0.234166 | -0.002375 | -0.070040 | 0.234887 | 0.689632 | 1.000000 | 0.163131 |
| Dataset | -0.137351 | -0.220208 | -0.246046 | -0.184866 | -0.163416 | -0.151934 | 0.035008 | 0.161388 | 0.163131 | 1.000000 |
Text(0.5, 1.0, 'Histogram plot to represent the number of different age group of peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Alkaline Phosphotase among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Aspartate Aminotransferase among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Alamine Aminotransferase among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Total Protiens among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Albumin among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Albumin and Globulin Ratio among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Total Bilirubin among the peoples')
Text(0.5, 1.0, 'Histogram plot to represent the rate of Direct Bilirubin among the peoples')
<seaborn.axisgrid.PairGrid at 0x25bbba93a90>
<seaborn.axisgrid.PairGrid at 0x25bc0571f70>
The pairplot comprises two figures namely, the histogram and the scatter plot. Histogram can be used to view the distribution of a single variable. Likewise, the scatter plots on the upper and lower triangles are used to view the relationship between two variables.
Text(0.5, 1.0, 'Pie-chart representing number of male and female present in the dataset')
Text(0.5, 1.0, 'Pie-chart representing number of patients with liver diseases and without liver disease in the dataset')
'plt.pie(liver_df.groupby(\'Dataset\')[\'Dataset\'].count(), \n explode=[0,0.3], \n labels=liver_df.groupby(\'Dataset\')[\'Dataset\'].count().index,\n colors=["#996699","#ff9999"],\n autopct="%.2f%%",\n radius=1.2,\n shadow = True\n )#grouping data as per the dataset column and counting the number of data as per dataset column for plotting a pie-chart \nplt.title("Pie-chart representing number of patients with liver diseases and without liver disease in the dataset") #defining title for the figure'
| Age | Count | |
|---|---|---|
| 50 | 60 | 26 |
| 23 | 32 | 18 |
| 36 | 45 | 18 |
| 41 | 50 | 17 |
| 37 | 46 | 16 |
| 39 | 48 | 16 |
| 31 | 40 | 15 |
| 46 | 55 | 15 |
| 64 | 75 | 14 |
| 33 | 42 | 13 |
| Age | Count | |
|---|---|---|
| 51 | 65 | 10 |
| 27 | 38 | 9 |
| 46 | 60 | 8 |
| 31 | 42 | 7 |
| 34 | 45 | 6 |
| 25 | 36 | 6 |
| 38 | 50 | 6 |
| 37 | 49 | 5 |
| 24 | 35 | 5 |
| 8 | 17 | 4 |
Text(0.5, 0.98, 'Bar Graph of top 10 count of patients with and without the liver disease as per the age group')
| Age | Count | |
|---|---|---|
| 35 | 44 | 2 |
| 34 | 43 | 2 |
| 51 | 61 | 2 |
| 57 | 67 | 1 |
| 65 | 78 | 1 |
| 59 | 69 | 1 |
| 0 | 7 | 1 |
| 53 | 63 | 1 |
| 47 | 56 | 1 |
| 1 | 8 | 1 |
| 18 | 27 | 1 |
| 14 | 23 | 1 |
| 11 | 20 | 1 |
| 10 | 19 | 1 |
| 8 | 17 | 1 |
| 6 | 15 | 1 |
| 5 | 14 | 1 |
| 3 | 12 | 1 |
| 2 | 10 | 1 |
| 66 | 90 | 1 |
| Age | Count | |
|---|---|---|
| 22 | 32 | 2 |
| 49 | 63 | 1 |
| 44 | 57 | 1 |
| 53 | 69 | 1 |
| 55 | 72 | 1 |
| 56 | 84 | 1 |
| 29 | 40 | 1 |
| 41 | 54 | 1 |
| 35 | 47 | 1 |
| 33 | 44 | 1 |
| 1 | 6 | 1 |
| 28 | 39 | 1 |
| 10 | 19 | 1 |
| 7 | 16 | 1 |
| 6 | 14 | 1 |
| 5 | 13 | 1 |
| 4 | 12 | 1 |
| 3 | 11 | 1 |
| 2 | 7 | 1 |
| 57 | 85 | 1 |
Text(0.5, 0.98, 'Bar Graph of top 20 lowest count of patients with and without the liver disease as per the age group')
Note:
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 13 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 dtypes: float64(5), int64(6), object(2) memory usage: 57.6+ KB
0 0.7
1 10.9
2 7.3
3 1.0
4 3.9
...
561 0.5
562 0.6
563 0.8
564 1.3
565 1.0
Name: Total_Bilirubin, Length: 566, dtype: float64
| Total_Bilirubin | Total_Bilirubin_Binary | |
|---|---|---|
| 0 | 0.7 | 1 |
| 1 | 10.9 | 0 |
| 2 | 7.3 | 0 |
| 3 | 1.0 | 1 |
| 4 | 3.9 | 0 |
| ... | ... | ... |
| 561 | 0.5 | 1 |
| 562 | 0.6 | 1 |
| 563 | 0.8 | 1 |
| 564 | 1.3 | 0 |
| 565 | 1.0 | 1 |
566 rows × 2 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 dtypes: float64(5), int64(7), object(2) memory usage: 62.0+ KB
| Total_Bilirubin | Total_Bilirubin_Binary | Total_Bilirubin_Description | |
|---|---|---|---|
| 0 | 0.7 | 1 | Normal |
| 1 | 10.9 | 0 | Not in Normal Range |
| 2 | 7.3 | 0 | Not in Normal Range |
| 3 | 1.0 | 1 | Normal |
| 4 | 3.9 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 0.5 | 1 | Normal |
| 562 | 0.6 | 1 | Normal |
| 563 | 0.8 | 1 | Normal |
| 564 | 1.3 | 0 | Not in Normal Range |
| 565 | 1.0 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object dtypes: float64(5), int64(7), object(3) memory usage: 66.5+ KB
Age 7 1 13 1 14 1 16 2 18 3 19 1 20 2 21 2 22 5 23 2 24 1 26 8 27 1 29 1 30 2 31 2 32 13 33 10 34 5 35 4 36 4 37 3 38 10 39 4 40 9 41 3 42 8 43 2 44 1 45 14 46 10 47 3 48 9 49 4 50 12 51 4 52 2 53 2 54 5 55 10 56 2 57 4 58 4 60 27 61 1 62 6 64 4 65 6 66 7 67 1 68 1 70 6 72 3 73 2 74 1 75 9 90 1 Name: Total_Bilirubin_Binary, dtype: int64
<matplotlib.legend.Legend at 0x25bca1c9c40>
| Age | Count | |
|---|---|---|
| 43 | 60 | 27 |
| 29 | 45 | 14 |
| 16 | 32 | 13 |
| 34 | 50 | 12 |
| 39 | 55 | 10 |
| 30 | 46 | 10 |
| 22 | 38 | 10 |
| 17 | 33 | 10 |
| 24 | 40 | 9 |
| 55 | 75 | 9 |
<matplotlib.legend.Legend at 0x25bca541a90>
Age
4 2
6 1
7 1
8 1
10 1
..
74 3
75 5
78 1
84 1
85 1
Name: Total_Bilirubin_Binary, Length: 69, dtype: int64
<matplotlib.legend.Legend at 0x25bca511d30>
Gender Female 36 Male 235 Name: Total_Bilirubin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Builirubin in recommended level as per the gender')
Gender Female 102 Male 193 Name: Total_Bilirubin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Builirubin in recommended level as per the gender')
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Details | Gender_Binary | Total_Bilirubin_Binary | Total_Bilirubin_Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | Patient with liver disease | 0 | 1 | Normal |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 | Patient with no liver disease | 1 | 1 | Normal |
| 562 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 | Patient with liver disease | 1 | 1 | Normal |
| 563 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal |
| 564 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range |
| 565 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 | Patient with no liver disease | 1 | 1 | Normal |
566 rows × 15 columns
Text(0.5, 0.98, 'Bar graph of Normal level Total Bilrubin vs Total Bilrubin not in normal level as per the gender')
Gender Female 60 Male 116 Name: Total_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Builirubin in recommended level and have liver disease as per the gender')
Gender Female 42 Male 77 Name: Total_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Builirubin in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Total Bilrubin in normal level and having liver disease VS Total Bilrubin in normal level and not having a liver disease')
Gender Female 30 Male 198 Name: Total_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Bilrubin in normal level and have liver diesease as per the gender')
Gender Female 6 Male 37 Name: Total_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Bilrubin in normal level and do not have liver diesease as per the gender')
Text(0.5, 0.98, 'Bar graph of Total Bilrubin not in normal level and having liver disease VS Total Bilrubin not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 15 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object dtypes: float64(5), int64(7), object(3) memory usage: 66.5+ KB
0 0.1
1 5.5
2 4.1
3 0.4
4 2.0
...
561 0.1
562 0.1
563 0.2
564 0.5
565 0.3
Name: Direct_Bilirubin, Length: 566, dtype: float64
| Direct_Bilirubin | Direct_Bilirubin_Binary | |
|---|---|---|
| 0 | 0.1 | 1 |
| 1 | 5.5 | 0 |
| 2 | 4.1 | 0 |
| 3 | 0.4 | 1 |
| 4 | 2.0 | 0 |
| ... | ... | ... |
| 561 | 0.1 | 1 |
| 562 | 0.1 | 1 |
| 563 | 0.2 | 1 |
| 564 | 0.5 | 0 |
| 565 | 0.3 | 1 |
566 rows × 2 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 16 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 dtypes: float64(5), int64(8), object(3) memory usage: 70.9+ KB
| Direct_Bilirubin | Direct_Bilirubin_Binary | Direct_Bilirubin_Description | |
|---|---|---|---|
| 0 | 0.1 | 1 | Normal |
| 1 | 5.5 | 0 | Not in Normal Range |
| 2 | 4.1 | 0 | Not in Normal Range |
| 3 | 0.4 | 1 | Normal |
| 4 | 2.0 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 0.1 | 1 | Normal |
| 562 | 0.1 | 1 | Normal |
| 563 | 0.2 | 1 | Normal |
| 564 | 0.5 | 0 | Not in Normal Range |
| 565 | 0.3 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object dtypes: float64(5), int64(8), object(4) memory usage: 75.3+ KB
Age 7 1 13 1 14 1 16 2 18 3 19 1 20 2 21 2 22 4 23 2 24 1 26 7 31 1 32 13 33 10 34 5 35 4 36 3 37 2 38 8 39 4 40 8 41 3 42 8 43 2 44 1 45 13 46 9 47 3 48 10 49 3 50 11 51 4 52 2 53 2 54 5 55 9 56 2 57 3 58 5 60 27 61 1 62 5 64 3 65 5 66 7 67 1 68 1 70 4 72 3 73 2 75 8 Name: Direct_Bilirubin_Binary, dtype: int64
<matplotlib.legend.Legend at 0x25bca818220>
| Age | Count | |
|---|---|---|
| 40 | 60 | 27 |
| 26 | 45 | 13 |
| 13 | 32 | 13 |
| 31 | 50 | 11 |
| 29 | 48 | 10 |
| 14 | 33 | 10 |
| 36 | 55 | 9 |
| 27 | 46 | 9 |
| 23 | 42 | 8 |
| 21 | 40 | 8 |
<matplotlib.legend.Legend at 0x25bc8615970>
Age
4 2
6 1
7 1
8 1
10 1
..
75 6
78 1
84 1
85 1
90 1
Name: Direct_Bilirubin_Binary, Length: 70, dtype: int64
<matplotlib.legend.Legend at 0x25bb9e8cfd0>
Gender Female 33 Male 214 Name: Direct_Bilirubin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Direct Builirubin in recommended level as per the gender')
Gender Female 105 Male 214 Name: Direct_Bilirubin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Direct Builirubin in recommended level as per the gender')
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Details | Gender_Binary | Total_Bilirubin_Binary | Total_Bilirubin_Description | Direct_Bilirubin_Binary | Direct_Bilirubin_Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | Patient with liver disease | 0 | 1 | Normal | 1 | Normal |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal |
| 562 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal |
| 563 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal |
| 564 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 565 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal |
566 rows × 17 columns
Text(0.5, 0.98, 'Bar graph of Normal level Direct Bilrubin VS Direct Bilrubin not in normal level as per the gender')
Gender Female 61 Male 130 Name: Direct_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Direct Builirubin in recommended level and have liver disease as per the gender')
Gender Female 44 Male 84 Name: Direct_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Direct Builirubin in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Direct Bilrubin in normal level and having liver disease VS Direct Bilrubin in normal level and not having a liver disease')
Gender Female 29 Male 184 Name: Direct_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Direct Bilrubin in normal level and have liver diesease as per the gender')
Gender Female 4 Male 30 Name: Direct_Bilirubin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Direct Bilrubin in normal level and do not have liver diesease as per the gender')
Text(0.5, 0.98, 'Bar graph of Direct Bilrubin not in normal level and having liver disease VS Direct Bilrubin not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 17 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object dtypes: float64(5), int64(8), object(4) memory usage: 75.3+ KB
0 187
1 699
2 490
3 182
4 195
...
561 500
562 98
563 245
564 184
565 216
Name: Alkaline_Phosphotase, Length: 566, dtype: int64
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 dtypes: float64(5), int64(9), object(4) memory usage: 79.7+ KB
| Alkaline_Phosphotase | Alkaline_Phosphotase_Binary | |
|---|---|---|
| 0 | 187 | 0 |
| 1 | 699 | 0 |
| 2 | 490 | 0 |
| 3 | 182 | 0 |
| 4 | 195 | 0 |
| ... | ... | ... |
| 561 | 500 | 0 |
| 562 | 98 | 1 |
| 563 | 245 | 0 |
| 564 | 184 | 0 |
| 565 | 216 | 0 |
566 rows × 2 columns
| Alkaline_Phosphotase | Alkaline_Phosphotase_Binary | Alkaline_Phosphotase_Description | |
|---|---|---|---|
| 0 | 187 | 0 | Not in Normal Range |
| 1 | 699 | 0 | Not in Normal Range |
| 2 | 490 | 0 | Not in Normal Range |
| 3 | 182 | 0 | Not in Normal Range |
| 4 | 195 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 500 | 0 | Not in Normal Range |
| 562 | 98 | 1 | Normal |
| 563 | 245 | 0 | Not in Normal Range |
| 564 | 184 | 0 | Not in Normal Range |
| 565 | 216 | 0 | Not in Normal Range |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object dtypes: float64(5), int64(9), object(5) memory usage: 84.1+ KB
Age
4 2
6 1
7 2
8 1
10 1
..
75 14
78 1
84 1
85 1
90 1
Name: Alkaline_Phosphotase_Binary, Length: 72, dtype: int64
<matplotlib.legend.Legend at 0x25bcb210f10>
| Age | Count | |
|---|---|---|
| 53 | 60 | 34 |
| 39 | 45 | 22 |
| 44 | 50 | 21 |
| 26 | 32 | 19 |
| 36 | 42 | 18 |
| 32 | 38 | 17 |
| 49 | 55 | 17 |
| 42 | 48 | 17 |
| 58 | 65 | 16 |
| 40 | 46 | 15 |
<matplotlib.legend.Legend at 0x25bcbb139d0>
Age 17 1 20 1 21 3 22 1 25 2 26 5 28 2 29 1 30 1 32 1 33 1 35 1 36 1 37 3 38 3 40 1 41 1 42 2 43 2 45 2 46 1 48 3 49 1 50 2 52 1 55 1 56 1 58 3 61 1 62 1 64 1 65 1 66 1 68 1 69 1 70 1 72 2 Name: Alkaline_Phosphotase_Binary, dtype: int64
<matplotlib.legend.Legend at 0x25bcbac72b0>
Gender Female 122 Male 386 Name: Alkaline_Phosphotase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alkaline Phosphotase in recommended level as per the gender')
Gender Female 16 Male 42 Name: Alkaline_Phosphotase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Alkaline Phosphotase (ALP) in recommended level as per the gender')
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Details | Gender_Binary | Total_Bilirubin_Binary | Total_Bilirubin_Description | Direct_Bilirubin_Binary | Direct_Bilirubin_Description | Alkaline_Phosphotase_Binary | Alkaline_Phosphotase_Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | Patient with liver disease | 0 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range |
| 562 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 1 | Normal |
| 563 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range |
| 564 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 565 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range |
566 rows × 19 columns
Text(0.5, 0.98, 'Bar graph of Normal level Alkaline Phosphotase (ALP) vs Alkaline Phosphotase (ALP) not in normal level as per the gender')
Gender Female 8 Male 28 Name: Alkaline_Phosphotase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Alkaline Phosphotase (ALP) in recommended level and have liver disease as per the gender')
Gender Female 8 Male 14 Name: Alkaline_Phosphotase_Description, dtype: int64
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Text(0.5, 0.98, 'Bar graph of Normal level Alkaline Phosphotase (ALP) in normal level and having liver disease VS Alkaline Phosphotase (ALP) in normal level and not having a liver disease')
Gender Female 82 Male 286 Name: Alkaline_Phosphotase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alkaline Phosphotase in normal level and have liver diesease as per the gender')
Gender Female 40 Male 100 Name: Alkaline_Phosphotase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alkaline Phosphotase (ALP) in normal level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Alkaline Phosphotase (ALP) not in normal level and having liver disease VS Alkaline Phosphotase (ALP) not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 19 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object dtypes: float64(5), int64(9), object(5) memory usage: 84.1+ KB
0 16
1 64
2 60
3 14
4 27
..
561 20
562 35
563 48
564 29
565 21
Name: Alamine_Aminotransferase, Length: 566, dtype: int64
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 dtypes: float64(5), int64(10), object(5) memory usage: 88.6+ KB
| Alamine_Aminotransferase | Alamine_Aminotransferase_Binary | |
|---|---|---|
| 0 | 16 | 1 |
| 1 | 64 | 0 |
| 2 | 60 | 0 |
| 3 | 14 | 1 |
| 4 | 27 | 1 |
| ... | ... | ... |
| 561 | 20 | 1 |
| 562 | 35 | 1 |
| 563 | 48 | 0 |
| 564 | 29 | 1 |
| 565 | 21 | 1 |
566 rows × 2 columns
| Alamine_Aminotransferase | Alamine_Aminotransferase_Binary | Alamine_Aminotransferase_Description | |
|---|---|---|---|
| 0 | 16 | 1 | Normal |
| 1 | 64 | 0 | Not in Normal Range |
| 2 | 60 | 0 | Not in Normal Range |
| 3 | 14 | 1 | Normal |
| 4 | 27 | 1 | Normal |
| ... | ... | ... | ... |
| 561 | 20 | 1 | Normal |
| 562 | 35 | 1 | Normal |
| 563 | 48 | 0 | Not in Normal Range |
| 564 | 29 | 1 | Normal |
| 565 | 21 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object dtypes: float64(5), int64(10), object(6) memory usage: 93.0+ KB
Age
4 1
6 1
7 1
12 2
14 1
..
69 1
70 6
73 1
75 7
90 1
Name: Alamine_Aminotransferase_Binary, Length: 61, dtype: int64
<matplotlib.legend.Legend at 0x25bcf459280>
| Age | Count | |
|---|---|---|
| 47 | 60 | 23 |
| 21 | 32 | 16 |
| 33 | 45 | 12 |
| 27 | 38 | 11 |
| 43 | 55 | 10 |
| 38 | 50 | 10 |
| 29 | 40 | 10 |
| 15 | 26 | 9 |
| 36 | 48 | 9 |
| 22 | 33 | 8 |
<matplotlib.legend.Legend at 0x25bd13bb4c0>
Age
4 1
7 1
8 1
10 1
11 1
..
74 4
75 7
78 1
84 1
85 1
Name: Alamine_Aminotransferase_Binary, Length: 68, dtype: int64
<matplotlib.legend.Legend at 0x25bd13fa130>
Gender Female 45 Male 234 Name: Alamine_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alamine Aminotransferase (ALT) in recommended level as per the gender')
Gender Female 93 Male 194 Name: Alamine_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Alamine Aminotransferase (ALT) in recommended level as per the gender')
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | ... | Dataset_Details | Gender_Binary | Total_Bilirubin_Binary | Total_Bilirubin_Description | Direct_Bilirubin_Binary | Direct_Bilirubin_Description | Alkaline_Phosphotase_Binary | Alkaline_Phosphotase_Description | Alamine_Aminotransferase_Binary | Alamine_Aminotransferase_Description | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | ... | Patient with liver disease | 0 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range | 1 | Normal |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | ... | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | ... | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | ... | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range | 1 | Normal |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | ... | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range | 1 | Normal |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 561 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | ... | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range | 1 | Normal |
| 562 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | ... | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 1 | Normal | 1 | Normal |
| 563 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | ... | Patient with liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range | 0 | Not in Normal Range |
| 564 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | ... | Patient with liver disease | 1 | 0 | Not in Normal Range | 0 | Not in Normal Range | 0 | Not in Normal Range | 1 | Normal |
| 565 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | ... | Patient with no liver disease | 1 | 1 | Normal | 1 | Normal | 0 | Not in Normal Range | 1 | Normal |
566 rows × 21 columns
Text(0.5, 0.98, 'Bar graph of Normal level Alamine Aminotransferase (ALT) vs Alamine Aminotransferase (ALT) not in normal level as per the gender')
Gender Female 54 Male 118 Name: Alamine_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Alamine Aminotransferase (ALT) in recommended level and have liver disease as per the gender')
Gender Female 39 Male 76 Name: Alamine_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Alamine Aminotransferase (ALT) in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Alamine Aminotransferase (ALT) in normal level and having liver disease VS Alamine Aminotransferase (ALT) in normal level and not having a liver disease')
Gender Female 36 Male 196 Name: Alamine_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alamine Aminotransferase (ALT) in normal level and have liver diesease as per the gender')
Gender Female 9 Male 38 Name: Alamine_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Alamine Aminotransferase (ALT) in normal level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Alamine Aminotransferase (ALT) not in normal level and having liver disease VS Alamine Aminotransferase (ALT) not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object dtypes: float64(5), int64(10), object(6) memory usage: 93.0+ KB
0 18
1 100
2 68
3 20
4 59
...
561 34
562 31
563 49
564 32
565 24
Name: Aspartate_Aminotransferase, Length: 566, dtype: int64
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 22 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 dtypes: float64(5), int64(11), object(6) memory usage: 97.4+ KB
| Aspartate_Aminotransferase | Aspartate_Aminotransferase_Binary | |
|---|---|---|
| 0 | 18 | 1 |
| 1 | 100 | 0 |
| 2 | 68 | 0 |
| 3 | 20 | 1 |
| 4 | 59 | 0 |
| ... | ... | ... |
| 561 | 34 | 1 |
| 562 | 31 | 1 |
| 563 | 49 | 0 |
| 564 | 32 | 1 |
| 565 | 24 | 1 |
566 rows × 2 columns
| Aspartate_Aminotransferase | Aspartate_Aminotransferase_Binary | Aspartate_Aminotransferase_Description | |
|---|---|---|---|
| 0 | 18 | 1 | Normal |
| 1 | 100 | 0 | Not in Normal Range |
| 2 | 68 | 0 | Not in Normal Range |
| 3 | 20 | 1 | Normal |
| 4 | 59 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 34 | 1 | Normal |
| 562 | 31 | 1 | Normal |
| 563 | 49 | 0 | Not in Normal Range |
| 564 | 32 | 1 | Normal |
| 565 | 24 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object dtypes: float64(5), int64(11), object(7) memory usage: 101.8+ KB
Age
4 1
7 2
8 1
10 1
12 2
..
73 2
74 1
75 4
78 1
90 1
Name: Aspartate_Aminotransferase_Binary, Length: 65, dtype: int64
<matplotlib.legend.Legend at 0x25bd31a0a00>
| Age | Count | |
|---|---|---|
| 49 | 60 | 21 |
| 22 | 32 | 16 |
| 35 | 45 | 15 |
| 38 | 48 | 12 |
| 45 | 55 | 11 |
| 30 | 40 | 11 |
| 28 | 38 | 11 |
| 40 | 50 | 10 |
| 32 | 42 | 9 |
| 36 | 46 | 9 |
<matplotlib.legend.Legend at 0x25bc88406d0>
Age
4 1
6 1
11 1
13 3
14 1
..
72 5
74 3
75 10
84 1
85 1
Name: Aspartate_Aminotransferase_Binary, Length: 61, dtype: int64
<matplotlib.legend.Legend at 0x25bd0c27af0>
Gender Female 49 Male 239 Name: Aspartate_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Aspartate Aminotransferase (AST) in recommended level as per the gender')
Gender Female 89 Male 189 Name: Aspartate_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Aspartate Aminotransferase (AST) in recommended level as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Aspartate Aminotransferase (AST) vs Aspartate Aminotransferase (AST) not in normal level as per the gender')
Gender Female 52 Male 113 Name: Aspartate_Aminotransferase_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Aspartate Aminotransferase (AST) in recommended level and have liver disease as per the gender')
Gender Female 37 Male 76 Name: Aspartate_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Aspartate Aminotransferase (AST) in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Aspartate Aminotransferase (AST) in normal level and having liver disease VS Aspartate Aminotransferase (AST) in normal level and not having a liver disease')
Gender Female 38 Male 201 Name: Aspartate_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Aspartate Aminotransferase (AST) in normal level and have liver disease as per the gender')
Gender Female 11 Male 38 Name: Aspartate_Aminotransferase_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Aspartate Aminotransferase (AST) in normal level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Aspartate Aminotransferase (AST) not in normal level and having liver disease VS Aspartate Aminotransferase (AST) not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object dtypes: float64(5), int64(11), object(7) memory usage: 101.8+ KB
0 6.8
1 7.5
2 7.0
3 6.8
4 7.3
...
561 5.9
562 6.0
563 6.4
564 6.8
565 7.3
Name: Total_Protiens, Length: 566, dtype: float64
| Total_Protiens | Total_Protiens_Binary | |
|---|---|---|
| 0 | 6.8 | 1 |
| 1 | 7.5 | 1 |
| 2 | 7.0 | 1 |
| 3 | 6.8 | 1 |
| 4 | 7.3 | 1 |
| ... | ... | ... |
| 561 | 5.9 | 0 |
| 562 | 6.0 | 1 |
| 563 | 6.4 | 1 |
| 564 | 6.8 | 1 |
| 565 | 7.3 | 1 |
566 rows × 2 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 24 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 dtypes: float64(5), int64(12), object(7) memory usage: 106.2+ KB
| Total_Protiens | Total_Protiens_Binary | Total_Protiens_Description | |
|---|---|---|---|
| 0 | 6.8 | 1 | Normal |
| 1 | 7.5 | 1 | Normal |
| 2 | 7.0 | 1 | Normal |
| 3 | 6.8 | 1 | Normal |
| 4 | 7.3 | 1 | Normal |
| ... | ... | ... | ... |
| 561 | 5.9 | 0 | Not in Normal Range |
| 562 | 6.0 | 1 | Normal |
| 563 | 6.4 | 1 | Normal |
| 564 | 6.8 | 1 | Normal |
| 565 | 7.3 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object dtypes: float64(5), int64(12), object(8) memory usage: 110.7+ KB
Age 6 1 13 1 14 1 16 1 18 3 19 1 20 1 21 1 22 3 24 1 25 1 26 6 27 2 28 3 29 3 30 5 31 1 32 8 33 3 34 4 35 2 36 5 37 7 38 3 39 2 40 6 42 9 44 1 45 12 46 4 47 1 48 9 49 4 50 5 51 2 52 1 53 2 54 1 55 5 56 2 57 5 58 5 60 11 61 2 62 4 63 1 64 1 65 9 66 4 68 2 69 1 70 4 72 1 73 1 74 2 75 9 Name: Total_Protiens_Binary, dtype: int64
<matplotlib.legend.Legend at 0x25bd6cefd00>
| Age | Count | |
|---|---|---|
| 28 | 45 | 12 |
| 42 | 60 | 11 |
| 26 | 42 | 9 |
| 47 | 65 | 9 |
| 31 | 48 | 9 |
| 55 | 75 | 9 |
| 17 | 32 | 8 |
| 22 | 37 | 7 |
| 11 | 26 | 6 |
| 25 | 40 | 6 |
<matplotlib.legend.Legend at 0x25bd70f87c0>
Age
4 2
7 2
8 1
10 1
11 1
..
75 5
78 1
84 1
85 1
90 1
Name: Total_Protiens_Binary, Length: 71, dtype: int64
<matplotlib.legend.Legend at 0x25bd7146760>
Gender Female 48 Male 147 Name: Total_Protiens_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Protiens in recommended level as per the gender')
Gender Female 90 Male 281 Name: Total_Protiens_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Protiens in recommended level as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Total Protiens vs Total Protiens not in normal level as per the gender')
Gender Female 60 Male 202 Name: Total_Protiens_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Protiens in recommended level and have liver disease as per the gender')
Gender Female 30 Male 79 Name: Total_Protiens_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Total Protiens in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Total Protiens in normal level and having liver disease VS Total Protiens in normal level and not having a liver disease')
Gender Female 30 Male 112 Name: Total_Protiens_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Protiens in normal level and have liver diesease as per the gender')
Gender Female 18 Male 35 Name: Total_Protiens_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Total Protiens in normal level and do not have liver diesease as per the gender')
Text(0.5, 0.98, 'Bar graph of Total Protiens not in normal level and having liver disease VS Total Protiens not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 25 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object dtypes: float64(5), int64(12), object(8) memory usage: 110.7+ KB
0 3.3
1 3.2
2 3.3
3 3.4
4 2.4
...
561 1.6
562 3.2
563 3.2
564 3.4
565 4.4
Name: Albumin, Length: 566, dtype: float64
| Albumin | Albumin_Binary | |
|---|---|---|
| 0 | 3.3 | 0 |
| 1 | 3.2 | 0 |
| 2 | 3.3 | 0 |
| 3 | 3.4 | 0 |
| 4 | 2.4 | 0 |
| ... | ... | ... |
| 561 | 1.6 | 0 |
| 562 | 3.2 | 0 |
| 563 | 3.2 | 0 |
| 564 | 3.4 | 0 |
| 565 | 4.4 | 1 |
566 rows × 2 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 26 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 dtypes: float64(5), int64(13), object(8) memory usage: 115.1+ KB
| Albumin | Albumin_Binary | Albumin_Description | |
|---|---|---|---|
| 0 | 3.3 | 0 | Not in Normal Range |
| 1 | 3.2 | 0 | Not in Normal Range |
| 2 | 3.3 | 0 | Not in Normal Range |
| 3 | 3.4 | 0 | Not in Normal Range |
| 4 | 2.4 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 1.6 | 0 | Not in Normal Range |
| 562 | 3.2 | 0 | Not in Normal Range |
| 563 | 3.2 | 0 | Not in Normal Range |
| 564 | 3.4 | 0 | Not in Normal Range |
| 565 | 4.4 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 27 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 26 Albumin_Description 566 non-null object dtypes: float64(5), int64(13), object(9) memory usage: 119.5+ KB
Age
4 1
6 1
7 1
8 1
16 1
..
74 2
75 13
78 1
84 1
90 1
Name: Albumin_Binary, Length: 64, dtype: int64
<matplotlib.legend.Legend at 0x25bd51bdd00>
| Age | Count | |
|---|---|---|
| 46 | 60 | 28 |
| 19 | 32 | 17 |
| 42 | 55 | 17 |
| 29 | 42 | 16 |
| 32 | 45 | 15 |
| 51 | 65 | 14 |
| 60 | 75 | 13 |
| 35 | 48 | 13 |
| 33 | 46 | 13 |
| 37 | 50 | 12 |
<matplotlib.legend.Legend at 0x25bda1d6bb0>
Age
4 1
7 1
10 1
11 1
12 2
..
70 1
72 1
74 2
75 1
85 1
Name: Albumin_Binary, Length: 65, dtype: int64
<matplotlib.legend.Legend at 0x25bd98abcd0>
Gender Female 77 Male 283 Name: Albumin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin in recommended level as per the gender')
Gender Female 61 Male 145 Name: Albumin_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin in recommended level as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Albumin vs Albumin not in normal level as per the gender')
Gender Female 38 Male 87 Name: Albumin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin in recommended level and have liver disease as per the gender')
Gender Female 23 Male 58 Name: Albumin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Albumin in normal level and having liver disease VS Albumin in normal level and not having a liver disease')
Gender Female 52 Male 227 Name: Albumin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin in normal level and have liver diesease as per the gender')
Gender Female 25 Male 56 Name: Albumin_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin in normal level and do not have liver diesease as per the gender')
Text(0.5, 0.98, 'Bar graph of Albumin not in normal level and having liver disease VS Albumin not in normal level and not having a liver disease')
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 27 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 26 Albumin_Description 566 non-null object dtypes: float64(5), int64(13), object(9) memory usage: 119.5+ KB
0 0.90
1 0.74
2 0.89
3 1.00
4 0.40
...
561 0.37
562 1.10
563 1.00
564 1.00
565 1.50
Name: Albumin_and_Globulin_Ratio, Length: 566, dtype: float64
| Albumin_and_Globulin_Ratio | Albumin_and_Globulin_Ratio_Binary | |
|---|---|---|
| 0 | 0.90 | 0 |
| 1 | 0.74 | 0 |
| 2 | 0.89 | 0 |
| 3 | 1.00 | 0 |
| 4 | 0.40 | 0 |
| ... | ... | ... |
| 561 | 0.37 | 0 |
| 562 | 1.10 | 0 |
| 563 | 1.00 | 0 |
| 564 | 1.00 | 0 |
| 565 | 1.50 | 1 |
566 rows × 2 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 28 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 26 Albumin_Description 566 non-null object 27 Albumin_and_Globulin_Ratio_Binary 566 non-null int64 dtypes: float64(5), int64(14), object(9) memory usage: 123.9+ KB
| Albumin_and_Globulin_Ratio | Albumin_and_Globulin_Ratio_Binary | Albumin_and_Globulin_Ratio_Description | |
|---|---|---|---|
| 0 | 0.90 | 0 | Not in Normal Range |
| 1 | 0.74 | 0 | Not in Normal Range |
| 2 | 0.89 | 0 | Not in Normal Range |
| 3 | 1.00 | 0 | Not in Normal Range |
| 4 | 0.40 | 0 | Not in Normal Range |
| ... | ... | ... | ... |
| 561 | 0.37 | 0 | Not in Normal Range |
| 562 | 1.10 | 0 | Not in Normal Range |
| 563 | 1.00 | 0 | Not in Normal Range |
| 564 | 1.00 | 0 | Not in Normal Range |
| 565 | 1.50 | 1 | Normal |
566 rows × 3 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 26 Albumin_Description 566 non-null object 27 Albumin_and_Globulin_Ratio_Binary 566 non-null int64 28 Albumin_and_Globulin_Ratio_Description 566 non-null object dtypes: float64(5), int64(14), object(10) memory usage: 128.4+ KB
Age
4 2
6 1
7 2
8 1
10 1
..
75 14
78 1
84 1
85 1
90 1
Name: Albumin_and_Globulin_Ratio_Binary, Length: 71, dtype: int64
<matplotlib.legend.Legend at 0x25bdb927c40>
| Age | Count | |
|---|---|---|
| 52 | 60 | 34 |
| 38 | 45 | 24 |
| 43 | 50 | 21 |
| 35 | 42 | 20 |
| 41 | 48 | 19 |
| 31 | 38 | 19 |
| 25 | 32 | 18 |
| 48 | 55 | 18 |
| 57 | 65 | 17 |
| 39 | 46 | 16 |
<matplotlib.legend.Legend at 0x25bd98adc40>
Age 15 1 17 2 24 1 25 2 27 1 28 2 29 1 31 1 32 2 33 1 35 1 37 1 38 1 40 1 43 1 48 1 49 2 50 2 53 1 54 1 62 2 63 1 66 1 68 1 70 1 Name: Albumin_and_Globulin_Ratio_Binary, dtype: int64
<matplotlib.legend.Legend at 0x25bdbe82d30>
Gender Female 130 Male 404 Name: Albumin_and_Globulin_Ratio_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin and Globulin Ratio in recommended level as per the gender')
Gender Female 8 Male 24 Name: Albumin_and_Globulin_Ratio_Binary, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin and Globulin Ratio in recommended level as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Albumin and Globulin Ratio vs Albumin and Globulin Ratio not in normal level as per the gender')
Gender Female 5 Male 15 Name: Albumin_and_Globulin_Ratio_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin and Globulin Ratio in recommended level and have liver disease as per the gender')
Gender Female 3 Male 9 Name: Albumin_and_Globulin_Ratio_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who have Albumin and Globulin Ratio in recommended level and do not have liver disease as per the gender')
Text(0.5, 0.98, 'Bar graph of Normal level Albumin and Globulin Ratio in normal level and having liver disease VS Albumin in normal level and not having a liver disease')
Gender Female 85 Male 299 Name: Albumin_and_Globulin_Ratio_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin and Globulin Ratio in normal level and have liver diesease as per the gender')
Gender Female 45 Male 105 Name: Albumin_and_Globulin_Ratio_Description, dtype: int64
Text(0.5, 1.0, 'Bar graph representing people who do not have Albumin and Globulin Ratio in normal level and do not have liver diesease as per the gender')
Text(0.5, 0.98, 'Bar graph of Albumin and Globulin Ratio not in normal level and having liver disease VS Albumin and Globulin Ratio not in normal level and not having a liver disease')
<seaborn.axisgrid.PairGrid at 0x25bdf4700d0>
<seaborn.axisgrid.PairGrid at 0x25bee3a5af0>
The pairplot comprises two figures namely, the histogram and the scatter plot. Histogram can be used to view the distribution of a single variable. Likewise, the scatter plots on the upper and lower triangles are used to view the relationship between two variables.
Pearsons correlation between Total Bilirubin & Direct Bilirubin: 0.874 and p-value: 6.802449119535963e-179
There is a positive, strong correlation between the two features namely Total Bilirubin and Direct Bilirubin of the dataset.
<class 'pandas.core.frame.DataFrame'> RangeIndex: 566 entries, 0 to 565 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 566 non-null int64 1 Gender 566 non-null object 2 Total_Bilirubin 566 non-null float64 3 Direct_Bilirubin 566 non-null float64 4 Alkaline_Phosphotase 566 non-null int64 5 Alamine_Aminotransferase 566 non-null int64 6 Aspartate_Aminotransferase 566 non-null int64 7 Total_Protiens 566 non-null float64 8 Albumin 566 non-null float64 9 Albumin_and_Globulin_Ratio 566 non-null float64 10 Dataset 566 non-null int64 11 Dataset_Details 566 non-null object 12 Gender_Binary 566 non-null int64 13 Total_Bilirubin_Binary 566 non-null int64 14 Total_Bilirubin_Description 566 non-null object 15 Direct_Bilirubin_Binary 566 non-null int64 16 Direct_Bilirubin_Description 566 non-null object 17 Alkaline_Phosphotase_Binary 566 non-null int64 18 Alkaline_Phosphotase_Description 566 non-null object 19 Alamine_Aminotransferase_Binary 566 non-null int64 20 Alamine_Aminotransferase_Description 566 non-null object 21 Aspartate_Aminotransferase_Binary 566 non-null int64 22 Aspartate_Aminotransferase_Description 566 non-null object 23 Total_Protiens_Binary 566 non-null int64 24 Total_Protiens_Description 566 non-null object 25 Albumin_Binary 566 non-null int64 26 Albumin_Description 566 non-null object 27 Albumin_and_Globulin_Ratio_Binary 566 non-null int64 28 Albumin_and_Globulin_Ratio_Description 566 non-null object dtypes: float64(5), int64(14), object(10) memory usage: 128.4+ KB
Pearsons correlation between Alamine Aminotransferase & Aspartate Aminotransferase: 0.792 and p-value: 7.764611510959146e-123
There is a positive, strong correlation between the two features namely Alamine Aminotransferase and Aspartate Aminotransferase of the dataset.
Pearsons correlation between Total Protiens & Albumin 0.784 and p-value: 8.711597342630112e-119
There is a positive, strong correlation between the two features namely Total Protiens and Albumin of the dataset.
Pearsons correlation between Albumin and Globulin Ratio & Albumin 0.687 and p-value: 2.1793959764618003e-80
There is a positive, strong correlation between the two features namely Albumin and Albumin and Globulin Ratio of the dataset.
Pearsons correlation between Albumin and Globulin Ratio & Total_Protiens 0.235 and p-value: 1.6291299405240473e-08
There is a moderate correlation between the two features namely Total Protiens & Albumin and Globulin Ratio of the dataset.
Conclusion
<class 'pandas.core.frame.DataFrame'> RangeIndex: 583 entries, 0 to 582 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 583 non-null int64 1 Gender 583 non-null object 2 Total_Bilirubin 583 non-null float64 3 Direct_Bilirubin 583 non-null float64 4 Alkaline_Phosphotase 583 non-null int64 5 Alamine_Aminotransferase 583 non-null int64 6 Aspartate_Aminotransferase 583 non-null int64 7 Total_Protiens 583 non-null float64 8 Albumin 583 non-null float64 9 Albumin_and_Globulin_Ratio 579 non-null float64 10 Dataset 583 non-null int64 dtypes: float64(5), int64(5), object(1) memory usage: 50.2+ KB
Text(0.5, 0.98, 'Boxplot of Total Bilirubin | Direct Bilirubin')
Text(0.5, 0.98, 'Boxplot of Total Protiens | Albumin | Albumin and Globulin Ratio')
Text(0.5, 0.98, 'Boxplot of Aspartate Aminotransferase | Alamine Aminotransferase | Alkaline Phosphotase')
Here, the prediction is being carried out in order to determine if the patient has an unhealthy Liver or not. Hence, the Outcome will be the y label and rest of the data will be the X or the input data.
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 |
| 579 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 580 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 581 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 582 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 |
583 rows × 11 columns
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 |
| 579 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 580 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 581 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 582 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 |
583 rows × 11 columns
Dataset Column
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset | Dataset_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 578 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 2 | 0 |
| 579 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 | 1 |
| 580 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 | 1 |
| 581 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 | 1 |
| 582 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 2 | 0 |
583 rows × 12 columns
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1161 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 0 |
| 1162 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 1163 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 1164 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 1165 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 0 |
1166 rows × 11 columns
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1161 | 60 | Male | 0.5 | 0.1 | 500 | 20 | 34 | 5.9 | 1.6 | 0.37 | 0 |
| 1162 | 40 | Male | 0.6 | 0.1 | 98 | 35 | 31 | 6.0 | 3.2 | 1.10 | 1 |
| 1163 | 52 | Male | 0.8 | 0.2 | 245 | 48 | 49 | 6.4 | 3.2 | 1.00 | 1 |
| 1164 | 31 | Male | 1.3 | 0.5 | 184 | 29 | 32 | 6.8 | 3.4 | 1.00 | 1 |
| 1165 | 38 | Male | 1.0 | 0.3 | 216 | 21 | 24 | 7.3 | 4.4 | 1.50 | 0 |
1158 rows × 11 columns
| Age | Gender | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset_Binary | Gender_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | Female | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | 0 |
| 1 | 62 | Male | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | 1 |
| 2 | 62 | Male | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | 1 |
| 3 | 58 | Male | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | 1 |
| 4 | 72 | Male | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | 1 |
| Age | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Dataset_Binary | Gender_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 1 | 0 |
| 1 | 62 | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 | 1 |
| 2 | 62 | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 | 1 |
| 3 | 58 | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 | 1 |
| 4 | 72 | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 | 1 |
| Age | Total_Bilirubin | Direct_Bilirubin | Alkaline_Phosphotase | Alamine_Aminotransferase | Aspartate_Aminotransferase | Total_Protiens | Albumin | Albumin_and_Globulin_Ratio | Gender_Binary | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65 | 0.7 | 0.1 | 187 | 16 | 18 | 6.8 | 3.3 | 0.90 | 0 |
| 1 | 62 | 10.9 | 5.5 | 699 | 64 | 100 | 7.5 | 3.2 | 0.74 | 1 |
| 2 | 62 | 7.3 | 4.1 | 490 | 60 | 68 | 7.0 | 3.3 | 0.89 | 1 |
| 3 | 58 | 1.0 | 0.4 | 182 | 14 | 20 | 6.8 | 3.4 | 1.00 | 1 |
| 4 | 72 | 3.9 | 2.0 | 195 | 27 | 59 | 7.3 | 2.4 | 0.40 | 1 |
0 1 1 1 2 1 3 1 4 1 Name: Dataset_Binary, dtype: int64
(926, 232)
StandardScalar transforms the data in such a manner that it has mean as 0 and standard deviation as 1. In short, it standardizes the data.
Standardize features by removing the mean and scaling to unit variance.
LogisticRegression()
Accuracy obtained by Logistic Regression model: 77.58620689655173
Text(0.5, 1.03, 'Confusion Matrix for Logistic Regression')
precision recall f1-score support
0 0.58 0.36 0.45 58
1 0.81 0.91 0.86 174
accuracy 0.78 232
macro avg 0.70 0.64 0.65 232
weighted avg 0.75 0.78 0.76 232
RandomForestClassifier()
Accuracy obtained by Random Forest Classifier model: 93.96551724137932
precision recall f1-score support
0 0.84 0.93 0.89 58
1 0.98 0.94 0.96 174
accuracy 0.94 232
macro avg 0.91 0.94 0.92 232
weighted avg 0.94 0.94 0.94 232
KNeighborsClassifier(n_neighbors=4)
Accuracy obtained by K Neighbors Classifier model: 70.6896551724138
Text(0.5, 1.03, 'Confusion Matrix for K Neighbors Classifier')
precision recall f1-score support
0 0.45 0.72 0.55 58
1 0.88 0.70 0.78 174
accuracy 0.71 232
macro avg 0.67 0.71 0.67 232
weighted avg 0.77 0.71 0.72 232
DecisionTreeClassifier()
Accuracy obtained by Decision Tree Classifier model: 91.37931034482759
Text(0.5, 1.03, 'Confusion Matrix for Decision Tree Classifier')
precision recall f1-score support
0 0.77 0.93 0.84 58
1 0.98 0.91 0.94 174
accuracy 0.91 232
macro avg 0.87 0.92 0.89 232
weighted avg 0.92 0.91 0.92 232
Learning rate set to 0.5 0: learn: 0.6142972 total: 157ms remaining: 1.41s 1: learn: 0.5602948 total: 161ms remaining: 643ms 2: learn: 0.5207494 total: 163ms remaining: 380ms
3: learn: 0.5010780 total: 165ms remaining: 247ms 4: learn: 0.4765250 total: 167ms remaining: 167ms 5: learn: 0.4622501 total: 169ms remaining: 112ms 6: learn: 0.4535824 total: 172ms remaining: 73.6ms 7: learn: 0.4415049 total: 173ms remaining: 43.4ms 8: learn: 0.4315656 total: 176ms remaining: 19.5ms 9: learn: 0.4249749 total: 177ms remaining: 0us
<catboost.core.CatBoostClassifier at 0x25b88401a00>
Accuracy obtained by CatBoost Classifier model: 80.60344827586206
Text(0.5, 1.03, 'Confusion Matrix for CatBoost Classifier')
precision recall f1-score support
0 0.64 0.50 0.56 58
1 0.84 0.91 0.88 174
accuracy 0.81 232
macro avg 0.74 0.70 0.72 232
weighted avg 0.79 0.81 0.80 232
GradientBoostingClassifier()
Accuracy obtained by Gradient Boosting Classifier model: 88.79310344827587
Text(0.5, 1.03, 'Confusion Matrix for Gradient Boosting Classifier')
precision recall f1-score support
0 0.83 0.69 0.75 58
1 0.90 0.95 0.93 174
accuracy 0.89 232
macro avg 0.87 0.82 0.84 232
weighted avg 0.88 0.89 0.88 232
SVC(probability=True)
Accuracy obtained by Support vector machine: 76.29310344827587
Text(0.5, 1.03, 'Confusion Matrix for Support vector machine')
precision recall f1-score support
0 1.00 0.05 0.10 58
1 0.76 1.00 0.86 174
accuracy 0.76 232
macro avg 0.88 0.53 0.48 232
weighted avg 0.82 0.76 0.67 232
Text(0.5, 1.03, 'Model Comparison - Model Accuracy')
The above bar graph shows that Random Forest Classifier perform the best on the test set.